Педагогічні умови підготовки студентів факультетів мистецтв у процесі вивчення диригентсько-хорових дисциплін

The article examines the pedagogical conditions for the preparation of students of the faculties of arts for the formation of vocal-ensemble competence in the process of studying conducting and choral disciplines, namely: actualization of the motivation of students of the faculties of arts for constant creative growth; activation of dialogue interaction; systematic expansion of the music-pedagogical and creative-performance experience of future music teachers. The pedagogical condition of actualizing the motivation of art faculty students for constant creative growth is an important condition for the formation of vocal-ensemble competence of future music teachers, because motivation acts not only as a motivating force, but also is considered by us as a regulatory, controlling, evaluative, dynamic and procedural basis of musical performance activity. Pedagogical conditions for the activation of dialogic interaction in the process of music education are key, because the ability to establish constructive dialogues is one of the manifestations of the performing profession. At the same time, the process of mastering the profession is also built on interaction, on the formation of a dialogue between the teacher and the student. The third pedagogical condition for the systematic expansion of the musical-pedagogical and creative-performing experience of students of the faculties of arts extends to the theoretical knowledge of various concepts and approaches in education, which reflect various aspects of vocal and choral training.Keywords: students of arts faculties, pedagogical conditions, artistic education, conducting and choral disciplines, vocal and ensemble competence.В статті розглядаються педагогічні умови підготовки студентів факультетів мистецтв до формування вокально-ансамблевої компетентності в процесі вивчення диригентсько-хорових дисциплін, а саме: актуалізація мотивації студентів факультетів мистецтв до постійного творчого зростання; активізація діалогової взаємодії; систематичне розширення музично-педагогічного та творчо-виконавського досвіду майбутніх учителів музичного мистецтва.Педагогічна умова актуалізації мотивації студентів факультетів мистецтв до постійного творчого зростання є важливою умовою для формування вокально-ансамблевої компетентності майбутніх учителів музичного мистецтва, адже мотивація виступає не лише спонукаючою силою, а також розглядається нами як регуляційна, контролююча, оцінююча, динамічна і процесуальна основа музично-виконавської діяльності. Педагогічна умова активізації діалогової взаємодії у процесі музичного навчання є ключовою, бо уміння налагоджувати конструктивні діалоги є одним з проявів виконавської професії. Водночас, процесс оволодіння професією також побудований на взаємодії, на утворенні діалогу між викладачем і студентом. Третя педагогічна умова систематичного розширення музично-педагогічного та творчо-виконавського досвіду студентів факультетів мистецтв поширюється на теоретичне пізнання різноманітних концепцій і підходів у навчанні, які відображають різноманітні сторони вокально-хорової підготовки.Ключові слова: студенти факультетів мистецтв, педагогічні умови, мистецьке навчання, диригентсько-хорові дисципліни, вокально-ансамблева компетентність

AuE-IPA: An AU Engagement Based Infant Pain Assessment Method

Recent studies have found that pain in infancy has a significant impact on infant development, including psychological problems, possible brain injury, and pain sensitivity in adulthood. However, due to the lack of specialists and the fact that infants are unable to express verbally their experience of pain, it is difficult to assess infant pain. Most existing infant pain assessment systems directly apply adult methods to infants ignoring the differences between infant expressions and adult expressions. Meanwhile, as the study of facial action coding system continues to advance, the use of action units (AUs) opens up new possibilities for expression recognition and pain assessment. In this paper, a novel AuE-IPA method is proposed for assessing infant pain by leveraging different engagement levels of AUs. First, different engagement levels of AUs in infant pain are revealed, by analyzing the class activation map of an end-to-end pain assessment model. The intensities of top-engaged AUs are then used in a regression model for achieving automatic infant pain assessment. The model proposed is trained and experimented on YouTube Immunization dataset, YouTube Blood Test dataset, and iCOPEVid dataset. The experimental results show that our AuE-IPA method is more applicable to infants and possesses stronger generalization ability than end-to-end assessment model and the classic PSPI metric

The relation between the rheological properties of gels and the mechanical properties of their corresponding aerogels

A series of low density, highly porous clay/poly(vinyl alcohol) composite aerogels, incorporating ammonium alginate, were fabricated via a convenient and eco-friendly freeze drying method. It is significant to understand rheological properties of precursor gels because they directly affect the form of aerogels and their processing behaviors. The introduction of ammonium alginate impacted the rheological properties of colloidal gels and improved the mechanical performance of the subject aerogels. The specific compositions and processing conditions applied to those colloidal gel systems brought about different aerogel morphologies, which in turn translated into the observed mechanical properties. The bridge between gel rheologies and aerogel structures are established in the present workPostprint (published version

Spatially and Spectrally Consistent Deep Functional Maps

Cycle consistency has long been exploited as a powerful prior for jointly optimizing maps within a collection of shapes. In this paper, we investigate its utility in the approaches of Deep Functional Maps, which are considered state-of-the-art in non-rigid shape matching. We first justify that under certain conditions, the learned maps, when represented in the spectral domain, are already cycle consistent. Furthermore, we identify the discrepancy that spectrally consistent maps are not necessarily spatially, or point-wise, consistent. In light of this, we present a novel design of unsupervised Deep Functional Maps, which effectively enforces the harmony of learned maps under the spectral and the point-wise representation. By taking advantage of cycle consistency, our framework produces state-of-the-art results in mapping shapes even under significant distortions. Beyond that, by independently estimating maps in both spectral and spatial domains, our method naturally alleviates over-fitting in network training, yielding superior generalization performance and accuracy within an array of challenging tests for both near-isometric and non-isometric datasets. Codes are available at https://github.com/rqhuang88/Spatiallyand-Spectrally-Consistent-Deep-Functional-Maps.Comment: Accepted by ICCV202

Q-YOLO: Efficient Inference for Real-time Object Detection

Real-time object detection plays a vital role in various computer vision applications. However, deploying real-time object detectors on resource-constrained platforms poses challenges due to high computational and memory requirements. This paper describes a low-bit quantization method to build a highly efficient one-stage detector, dubbed as Q-YOLO, which can effectively address the performance degradation problem caused by activation distribution imbalance in traditional quantized YOLO models. Q-YOLO introduces a fully end-to-end Post-Training Quantization (PTQ) pipeline with a well-designed Unilateral Histogram-based (UH) activation quantization scheme, which determines the maximum truncation values through histogram analysis by minimizing the Mean Squared Error (MSE) quantization errors. Extensive experiments on the COCO dataset demonstrate the effectiveness of Q-YOLO, outperforming other PTQ methods while achieving a more favorable balance between accuracy and computational cost. This research contributes to advancing the efficient deployment of object detection models on resource-limited edge devices, enabling real-time detection with reduced computational and memory overhead

Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Analysis

Self-supervised learning (SSL) has recently achieved promising performance for 3D medical image analysis tasks. Most current methods follow existing SSL paradigm originally designed for photographic or natural images, which cannot explicitly and thoroughly exploit the intrinsic similar anatomical structures across varying medical images. This may in fact degrade the quality of learned deep representations by maximizing the similarity among features containing spatial misalignment information and different anatomical semantics. In this work, we propose a new self-supervised learning framework, namely Alice, that explicitly fulfills Anatomical invariance modeling and semantic alignment via elaborately combining discriminative and generative objectives. Alice introduces a new contrastive learning strategy which encourages the similarity between views that are diversely mined but with consistent high-level semantics, in order to learn invariant anatomical features. Moreover, we design a conditional anatomical feature alignment module to complement corrupted embeddings with globally matched semantics and inter-patch topology information, conditioned by the distribution of local image content, which permits to create better contrastive pairs. Our extensive quantitative experiments on three 3D medical image analysis tasks demonstrate and validate the performance superiority of Alice, surpassing the previous best SSL counterpart methods and showing promising ability for united representation learning. Codes are available at https://github.com/alibaba-damo-academy/alice.Comment: This paper has been accepted by ICCV 2023 (oral

Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis

BackgroundSepsis is a heterogeneous disease, therefore the single-gene-based biomarker is not sufficient to fully understand the disease. Higher-level biomarkers need to be explored to identify important pathways related to sepsis and evaluate their clinical significance.MethodsGene Set Enrichment Analysis (GSEA) was used to analyze the sepsis transcriptome to obtain the pathway-level expression. Limma was used to identify differentially expressed pathways. Tumor IMmune Estimation Resource (TIMER) was applied to estimate immune cell abundance. The Spearman correlation coefficient was used to find the relationships between pathways and immune cell abundance. Methylation and single-cell transcriptome data were also employed to identify important pathway genes. Log-rank test was performed to test the prognostic significance of pathways for patient survival probability. DSigDB was used to mine candidate drugs based on pathways. PyMol was used for 3-D structure visualization. LigPlot was used to plot the 2-D pose view for receptor-ligand interaction.ResultsEighty-four KEGG pathways were differentially expressed in sepsis patients compared to healthy controls. Of those, 10 pathways were associated with 28-day survival. Some pathways were significantly correlated with immune cell abundance and five pathways could be used to distinguish between systemic inflammatory response syndrome (SIRS), bacterial sepsis, and viral sepsis with Area Under the Curve (AUC) above 0.80. Seven related drugs were screened using survival-related pathways.ConclusionSepsis-related pathways can be utilized for disease subtyping, diagnosis, prognosis, and drug screening